Abstract Machine learning (ML) promises to tackle the grand challenges in chemistry and
speed up the generation, improvement and/or ordering of research hypotheses. Despite the …

Designing new medicines more cheaply and quickly is tightly linked to the quest of exploring
chemical space more widely and efficiently. Chemical space is monumentally large, but …

This work introduces a method to tune a sequence-based generative model for molecular de
novo design that through augmented episodic likelihood can learn to generate structures …

Multitargeting compounds comprising activity on more than a single biological target have
gained remarkable relevance in drug discovery owing to the complexity of multifactorial …

Natural products and their molecular frameworks have a long tradition as valuable starting
points for medicinal chemistry and drug discovery. Recently, there has been a revitalization …

Generative chemical language models (CLMs) can be used for de novo molecular structure
generation by learning from a textual representation of molecules. Here, we show that hybrid …

Docking is now routine in virtual screening or lead optimization for drug screening and
design. The number of papers related to docking has dramatically increased over the past …

Automating the molecular design-make-test-analyze cycle accelerates hit and lead finding
for drug discovery. Using deep learning for molecular design and a microfluidics platform for …

De novo drug design aims to generate molecules from scratch that possess specific
chemical and pharmacological properties. We present a computational approach utilizing …

Medicinal chemistry and, in particular, drug design have often been perceived as more of an
art than a science. The many unknowns of human disease and the sheer complexity of …